This application relates generally to gas turbine engines and, more particularly, to methods and apparatus for trending gas turbine engine operation.
As gas turbine engines operate, the engines may become less efficient due to a combination of factors including wear and damage. Because the rate at which engines deteriorate depends on several operational factors, the rate is difficult to predict, and as such, engine components are typically scheduled for maintenance based on a pre-selected number of hours or cycles. The pre-selected number is typically conservatively selected based on a number of factors including past component experience and past engine health estimates. If a component fails, a predetermined diagnosis routine is followed to identify and replace the failed component.
To estimate engine health and to find engine sensor faults, selected engine parameters are sensed and monitored to estimate an overall loss in engine performance. Typically, rotor speeds, exhaust gas temperatures, and fuel flows are corrected or normalized for variations in operating conditions, and these normalized parameters are trended, i.e., their changes over short and long periods of time are plotted, and used to forecast when engine refurbishment is required. Additionally, immediate engine repairs may be scheduled if comparing current trending values to prior trending values illustrates abrupt changes, or step changes.
Due to manufacturing tolerances, faults, damage, or deterioration with time, actual engine characteristics typically are different from the assumed nominal characteristics. Hence, the traditional normalized parameters may not be accurate. To facilitate improving the estimates of normalized sensor parameters as well as of other trended parameters, engine models and parameter estimation algorithms are used to track engine health and provide xe2x80x9chealth estimatesxe2x80x9d of engine components. Known trending estimation algorithms account for variations in operating conditions, but do not account for engine quality and engine deterioration effects. More specifically, because of the complexity of the computations, known correction factors and parameter estimation algorithms do not provide reliable estimations and trend parameters during real-time engine operation.
In an exemplary embodiment, a model-based trending process for a gas turbine engine generates, in real-time, engine trend parameters from engine sensor data and ambient flight condition data to assess engine condition. The engine includes a plurality of sensors that are responsive to engine operations. In an exemplary embodiment, the trending process is implemented using a commercially available processor coupled to the engine to monitor the engine operations, and having the desired processing speed and capacity.
The trending process estimates engine health parameters for use in a model for component diagnostics and fault detection and isolation. The interactions and physical relationships of trend parameters within the engine cycle are retained to permit substantially all sensed and model-generated virtual parameters for trending to be generated simultaneously. As a result, the trending process accounts for engine quality and deterioration effects and provides engine health estimates that facilitate improving estimates of performance parameters or xe2x80x9cvirtual sensorsxe2x80x9d for use in trending engine operation.